Abstract

The battery is a key component of electric vehicles. To reach the needed voltage and capacity, single Lithium-Ion cells are assembled into modules, then assembled into the pack. Their disassembly, which unlocks both remanufacturing or recycling and which nowadays is made mainly manually, has high electric hazards. Decision tools have not yet been developed to minimize these risks. This work presents a mathematical model to determine the disassembly sequence with the minimal exposure of the operator to hazardous voltages. The model considers the mechanical and electrical architecture of the battery and the tasks needed to reach the desired disassembly level.

Abstract

utomation of disassembly processes in electronic waste recycling is progressing but hindered by the lack of automated procedures for screw detection and removal. Here we specifically address the detection problem and implement a universal, generalizable, and extendable screw detector which can be deployed in automated disassembly lines. We selected the best performing state-of-the-art classifiers and compared their performance to that of our architecture, which combines a Hough transform with a novel integrated model of two deep convolutional neural networks for screw detection. We show that our method outperforms currently existing methods, while maintaining the high speed of computation. Data set and code of this study are made public.